Sunish Vengathattil

The healthcare sector is undergoing a digital transformation thanks to new technologies, with digital twinning and generative artificial intelligence (AI) leading the innovation. Digital twins, conceptualized originally as engineering or manufacturing tools, are increasingly finding their way to the healthcare sector, in response to the growing need for sophisticated virtual patient representations with scope for modeling several complex biological systems. Empowered by generative AI, digital twins, as they start to replace static models, open their gates into dynamic, predictive, prescriptive systems, enabling personalized healthcare delivery, disease modeling, surgical planning, and drug discovery. This paper reviews the combined potential of AI and digital twin technologies in the healthcare domain. It delivers a comprehensive view on the present possible applications, benefits, and opportunities of technology while putting in perspective the challenges regarding data privacy, ethical, computational, and design biases. By intertwining results from various studies and companies, the research thereby expounds into realizing the positive thrust capability of generative AI digital twins in influencing the transformation of healthcare delivery toward more stringent, predictive, preventive medicine. The paper awsd identifies future research directions crucial to confronting current challenges and ensuring the responsible deployment of these technologies in healthcare systems across the globe.

Choosing the right machine learning model for a given problem is a critical decision that significantly impacts the success of a project. This article provides a comprehensive guide to selecting the most appropriate model based on the problem, the available data, and operational constraints. It explores key considerations such as interpretability, data quality, computational resources, and real-time requirements while emphasizing the importance of aligning the model with business goals and stakeholder needs. Through case studies in areas like fraud detection, medical image classification, and customer segmentation, the article illustrates how different models are suited to specific types of problems. It also highlights the importance of deploying, monitoring, and maintaining models to ensure their long-term effectiveness. By presenting a structured approach to model selection and ongoing improvement, this article aims to provide valuable insights for practitioners seeking to optimize the use of machine learning in real-world applications.

The rapid growth of technology has created a strong demand for skilled professionals, requiring universities to adapt their programs to meet industry needs. This paper examines the role of higher education, especially in the United States, in preparing students for the future workforce by addressing key challenges such as keeping curricula up to date, securing research funding, and providing hands-on training. To tackle these issues, universities are introducing specialized programs, encouraging interdisciplinary learning, and building stronger partnerships with industries to close the skills gap. Additionally, new educational tools are being used to personalize learning and better equip students for technology-driven careers. This paper emphasizes that continuous innovation in higher education is essential to developing a well-prepared, industry-ready workforce, helping the U.S. maintain its leadership in the global economy.

Artificial Intelligence (AI) has become a crucial element of modern technological innovation, influencing industries as diverse as healthcare, finance, and transportation. However, AI remains widely misunderstood, with myths often overshadowing its true capabilities and limitations. Misconceptions range from fears of AI surpassing human intelligence to unrealistic expectations about its autonomy and decision-making abilities. This paper aims to clarify these misunderstandings by distinguishing between common AI myths and factual realities. By exploring AI's definitions, functionalities, implications, and societal impact, this article provides a nuanced understanding of AI’s current and future role. Addressing these myths will help businesses, policymakers, and the public make informed decisions regarding AI’s development and implementation.

Business continuity management provides holistic and proactive approaches for businesses to effectively manage the potential disruption caused by unforeseen events. Despite the benefits, most organizations are reluctant to adopt effective systems for BCP due to implementation technicalities, complacency, lack of tools and resources, and lack of training. This explorative study leverages case studies to examine and critically analyze how information systems can be leveraged to create, test, execute, and maintain BCPs in IT-driven companies. The case studies focus on the information systems leveraged by a purposively selected sample of Fortune 500 companies. The chosen companies include Walmart, Amazon, Apple, Microsoft, Toyota, General Motors, and IBM. The diversity and credibility of the findings are enhanced by incorporating systems used by a selected sample of large IT-driven companies not listed in the Fortune 500 list. The recommended strategies for using systems to reduce the cost of BCP include adopting cloud-based solutions, automation, virtualization, risk assessment, and standardization. The recommendations to educate organizations on the risks of operating without robust BCPs include awareness campaigns, real-world case studies, compliance requirements, and financial incentives. Implementing the proposed strategies enables organizations to leverage cost-effective, scalable, and reliable information systems to automate BCPs and operations, enhancing competitiveness and resilience to disruptions.

The rapid evolution of Information and Communications Technology (ICT) has significantly improved healthcare systems, yet interoperability challenges persist. Effective communication between healthcare information systems is critical for patient care, data security, and operational efficiency. However, corporate interests, lack of standardized communication, and inadequate regulatory enforcement hinder seamless data exchange. This paper examines the ethical implications of interoperability in healthcare, emphasizing the role of Corporate Social Responsibility (CSR) and legal frameworks in addressing these issues. It discusses challenges such as data privacy, vendor monopolies, and the prioritization of profits over patient well-being. Recommendations include fostering an ethical corporate culture, enforcing legal mandates like the 21st Century Cures Act, adopting standardized clinical terminologies, and implementing industry-wide interoperability standards such as HL7-FHIR. By aligning business goals with societal welfare, the healthcare IT industry can enhance patient outcomes, reduce medical errors, and improve overall healthcare efficiency.

This paper will research and report on the various network design and management trends for the past three years. The report will include several types of research concerning particular network design and administration; particularly virtualization, security, and network management tools. The paper will also discuss the trends with subsets of the mentioned areas of the network design and management in the Information Technology industry.

This paper delved into an ethical analysis of various applications of artificial intelligence (AI). It covers the application of AI in different sectors viz. healthcare, finance, manufacturing, automobile, and defense. In every industry, the questions and concerns that AI raise are discussed. Many times, actual events are cited as the evidence of the argument – which is noteworthy. It is argued that the ethical implementation of AI is not practical as of today. Though achievements of AI are embraced and admired, the paper concludes by stating that it is not possible to enforce ethics in AI with today’s technology. The paper also calls for action to hold strict governance to AI advancements until the ethical implementations are enforced.